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About Me
I have a total of more than 5 years of experience in the data science field. I have been working hands-on to solve critical business problems for Fortune 500 companies across multiple business sectors using ML, DL and statistics. I have experi...
My expertise lies in understanding/capturing the business problem, converting the business problem into an analytical problem and using the right technology to solve the problem. I am well versed with most of the core technologies and techniques in the data science domain. (Details in the skills/technologies section)
Some of the interesting business problems that I've worked on - Clusterig retail stores, Forecasting weekly sales for stores, Predicting attrition of employees, Predicting failure rate of hardware parts, Analyzing sentiments of tweets about an organisation, Picking relevant information from law book, Extracting text from natural scene images, Extracting entities from news articles etc.
Throughout my career, I have worked in core techniques like clustering, classification, regression, image classification, information retrieval, text classification, object detection etc.
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Description
Built an information retrieval model to pick the most relevant answers from the US tax law book for the input
question. Read and implemented the research paper “A Latent Semantic Model with Convolutional-Pooling
structure for Information Retrieval” from scratch in Pytorch.